Ambulatory Electrocardiology

Romero, Iñaki

doi:10.1097/crd.0b013e31828a8294

Cited by 9 publications

(4 citation statements)

References 81 publications

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“…In particular, the last few years saw an increasing number of studies on methods to compensate (Romero 2010), eliminate (Liu 2010), and otherwise detect (Lee et al 2012) body movement artifacts (BMAs) on ECG signals (Clifford and Moody 2012). However, very few of those studies have focused on the topic of quantifying these artifacts.…”

Section: Body Movement Artifacts On Ecg Signalsmentioning

confidence: 99%

Estimating actigraphy from motion artifacts in ECG and respiratory effort signals

et al. 2015

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Recent work in unobtrusive sleep/wake classification has shown that cardiac and respiratory features can help improve classification performance. Nevertheless, actigraphy remains the single most discriminative modality for this task. Unfortunately, it requires the use of dedicated devices in addition to the sensors used to measure electrocardiogram (ECG) or respiratory effort. This paper proposes a method to estimate actigraphy from the body movement artifacts present in the ECG and respiratory inductance plethysmography (RIP) based on the time-frequency analysis of those signals. Using a continuous wavelet transform to analyze RIP, and ECG and RIP combined, it provides a surrogate measure of actigraphy with moderate correlation (for ECG+RIP, 0.74 ρ = , p < 0.001) and agreement (mean bias ratio of 0.94 and 95% agreement ratios of 0.11 and 8.45) with reference actigraphy. More important, it can be used as a replacement of actigraphy in sleep/wake classification: after cross-validation with a data set comprising polysomnographic (PSG) recordings of 15 healthy subjects and 25 insomniacs annotated by an external sleep technician, it achieves a statistically non-inferior classification performance when used together with respiratory features (average κ of 0.64 for 15 healthy subjects, and 0.50 for a dataset with 40 healthy and insomniac subjects), and when used together with respiratory and cardiac features (average κ of 0.66 for 15 healthy subjects, and 0.56 for 40 healthy and insomniac subjects). Since this method eliminates the need for a dedicated actigraphy device, it reduces the number Institute of Physics and Engineering in MedicineContent from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. of sensors needed for sleep/wake classification to a single sensor when using respiratory features, and to two sensors when using respiratory and cardiac features without any loss in performance. It offers a major benefit in terms of comfort for long-term home monitoring and is immediately applicable for legacy ECG and RIP monitoring devices already used in clinical practice and which do not have an accelerometer built-in.

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Section: Body Movement Artifacts On Ecg Signalsmentioning

confidence: 99%

Estimating actigraphy from motion artifacts in ECG and respiratory effort signals

et al. 2015

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“…Long‐term ECG monitoring of AF could be a useful tool for evaluating the burden of AF and identifying asymptomatic AF. Current ambulatory ECG monitors permit continuous monitoring for up to 7 days or can record symptomatic or automatically detected AF episodes for up to 30 days . Existing AF detection algorithms still do not achieve high levels of sensitivity and specificity, especially due to motion artifacts and other noise within the ECG .…”

Section: Diagnosismentioning

confidence: 99%

Diagnosis and Management of Atrial Fibrillation: An Overview

Romero

Nedios

Kriatselis

2014

Cardiovascular Therapeutics

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Summary Atrial fibrillation (AF) has been defined as the new cardiovascular “epidemic”. Its prevalence is rising in developed countries, and the associated social and economic costs are high. In the last few years, there has been an increasing interest in understanding the mechanisms of AF and its management. New pharmacotherapies together with novel techniques for surgical and catheter treatment of AF have been developed, allowing the maintenance of sinus rhythm and the alleviation of symptoms in a large number of patients with AF. However, there are still some challenges that need to be addressed. This article gives an overview of the current state of the art on novel techniques for diagnosis and management of AF.

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“…While conventional filters can effectively eliminate most of these noise types, extracting a clean heartbeat from an ECG signal corrupted with “Additive White Gaussian Noise” (AWGN) remains a critical issue 12 . From the literature, there are four main methods for de‐noising ECG signals: Wavelet‐based methods, 13–15 PCA‐based methods, 16–18 Kalman filter‐based methods, 19,20 and artificial intelligence‐based methods (AI) 21–23 . The main limitation of the wavelet‐based approaches is the difficulty in selecting the appropriate wavelet basis and decomposition level.…”

Section: Introductionmentioning

confidence: 99%

An efficient wavelet thresholding strategy and robust shrinkage approach for de‐noising ECG signal

Ahmed

2023

Int J Imaging Syst Tech

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This paper introduces an efficient wavelet thresholding strategy and robust shrinkage approach (WTS) for de‐noising the ECG signals. The optimal mother wavelet for de‐noising the ECG signal is automatically selected based on cross‐correlation and energy‐to‐entropy indices. A dynamic threshold is applied to various levels of decomposition to eliminate different types of noise. A shrinkage approach is suggested to attain seamless on–off transitions. The achievement of the WTS is compared with the state of the art reported in the literature using 180 real ECG signals collected from 60 volunteers. The ECG signals were recorded using three standard ECG machines without applying the filtering option to provide a diverse range of noisy ECG signals. The de‐noising outcomes show that the presented WTS can automatically select the optimal mother wavelet for the particular ECG signal, and it is superior to other techniques in terms of the average predicted signal‐to‐noise ratio (), the average predicted root‐mean‐square error (), and the average execution time (AET).

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Ambulatory Electrocardiology

Cited by 9 publications

References 81 publications

Estimating actigraphy from motion artifacts in ECG and respiratory effort signals

Estimating actigraphy from motion artifacts in ECG and respiratory effort signals

Diagnosis and Management of Atrial Fibrillation: An Overview

An efficient wavelet thresholding strategy and robust shrinkage approach for de‐noising ECG signal

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